Data Science and Machine Learning using Python - A Bootcamp

About Data Science and Machine Learning using Python - A Bootcamp

_Greetings,_

I am so excited to learn that you have started your path to becoming a

_Data Scientist_

with my course. Data Scientist is in-demand and most satisfying career, where you will solve the most interesting problems and challenges in the world. Not only, you will earn average salary of over $100,000 p.a., you will also see the

_impact of your work around your, is not is amazing?_

This is

_one of the most comprehensive course on any e-learning platform_

_(including Udemy marketplace)_ Data Science Bootcamps are costly, in thousands of dollars. However, this course is only a fraction of the cost of any such Bootcamp and includes

_HD lectures_

along with detailed

_code notebooks_

for every lecture. The course also includes

_practice exercises on real data_

for each topic you cover, because the goal is "Learn by Doing"! For your satisfaction, I would like to mention few topics that we will be learning in this course:

_Basis Python programming for Data Science_

Data Types, Comparisons Operators, if, else, elif statement, Loops, List Comprehension, Functions, Lambda Expression, Map and Filter

_NumPy_

Arrays, built-in methods, array methods and attributes, Indexing, slicing, broadcasting & boolean masking, Arithmetic Operations & Universal Functions

_Pandas_

Pandas Data Structures - Series, DataFrame, Hierarchical Indexing, Handling Missing Data, Data Wrangling - Combining, merging, joining, Groupby, Other Useful Methods and Operations, Pandas Built-in Data Visualization

_Matplotlib_

Basic Plotting & Object Oriented Approach

_Seaborn_

Distribution & Categorical Plots, Axis Grids, Matrix Plots, Regression Plots, Controlling Figure Aesthetics

_Plotly and Cufflinks_

_Interactive & Geographical plotting_

_SciKit-Learn_

(one of the world's best machine learning Python library) including:

_Liner Regression_

Over fitting , Under fitting Bias Variance Trade-off, saving and loading your trained Machine Learning Models

_Logistic Regression_

Confusion Matrix, True Negatives/Positives, False Negatives/Positives, Accuracy, Misclassification Rate / Error Rate, Specificity, Precision

_K Nearest Neighbour (KNN)_

Curse of Dimensionality, Model Performance

_Decision Trees_

Tree Depth, Splitting at Nodes, Entropy, Information Gain

_Random Forests_

Bootstrap, Bagging (Bootstrap Aggregation)

_K Mean Clustering_

Elbow Method

_Principle Component Analysis (PCA)_

_Support Vector Machine_

_Recommender Systems_

_Natural Language Processing (NLP)_

Tokenization, Text Normalization, Vectorization, Bag-of-Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), Pipeline feature........and

MUCH MORE

..........! Not only the hands-on practice using tens of real data project, theory lectures are also provided to make you understand the working principle behind the Machine Learning models.

_So, what are you waiting for, this is your opportunity to learn the real Data Science with a fraction of the cost of any of your undergraduate course.....!_

_Brief overview of Data around us:_

According to IBM, we create 2.5 Quintillion bytes of data daily and 90% of the existing data in the world today, has been created in the last two years alone. Social media, transactions records, cell phones, GPS, emails, research, medical records and much more…., the data comes from everywhere which has created a big talent gap and the industry, across the globe, is experiencing shortage of experts who can answer and resolve the challenges associated with the data. Professionals are needed in the field of Data Science who are capable of handling and presenting the insights of the data to facilitate decision making. This is the time to get into this field with the knowledge and in-depth skills of data analysis and presentation.

Have Fun and Good Luck!